Articles | Volume 16, issue 9
Development and technical paper
05 May 2023
Development and technical paper |  | 05 May 2023

Emulating aerosol optics with randomly generated neural networks

Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-559', Anonymous Referee #1, 15 Jul 2022
  • RC2: 'Comment on egusphere-2022-559', Anonymous Referee #2, 10 Aug 2022
  • CEC1: 'Comment on egusphere-2022-559', Juan Antonio Añel, 23 Aug 2022
    • AC1: 'Reply on CEC1', Andrew Geiss, 06 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Andrew Geiss on behalf of the Authors (21 Feb 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (08 Mar 2023) by Samuel Remy
RR by Anonymous Referee #1 (18 Mar 2023)
ED: Publish as is (04 Apr 2023) by Samuel Remy
AR by Andrew Geiss on behalf of the Authors (07 Apr 2023)  Manuscript 
Short summary
Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.